Deep-learning informed design of unitary operators in silicon photonics using programmable phase change materials.

By integrating chalcogenide phase change materials to the fabrication of coupled silicon waveguide arrays we can manipulate the propagation of light through photonic devices, by the introducing nanoscale refractive index perturbations. Prediction of the required pixel pattern needed to produce a given unitary transmission matrix is a complex problem. In order to optimize for multiple input and outputs simultaneously we employ an artificial neural network, for both forward prediction and inverse design. This work hopes to pave the way towards all optical computation and the production of reconfigurable analogue quantum simulators.